7,193 research outputs found
Analysis of camera pose estimation using 2D scene features for augmented reality applications
La réalité augmentée (RA) a récemment eu un impact énorme sur les ingénieurs civils et les travailleurs de l'industrie de la construction, ainsi que sur leur interaction avec les plans ar-chitecturaux. La RA introduit une superposition du modèle 3D d'un bâtiment sur une image 2D non seulement comme une image globale, mais aussi potentiellement comme une repré-sentation complexe de ce qui va être construit et qui peut être visualisée par l'utilisateur. Pour insérer un modèle 3D, la caméra doit être localisée par rapport à son environnement. La lo-calisation de la caméra consiste à trouver les paramètres extérieurs de la caméra (i.e. sa po-sition et son orientation) par rapport à la scène observée et ses caractéristiques. Dans ce mémoire, des méthodes d'estimation de la pose de la caméra (position et orientation) par rapport à la scène utilisant des correspondances cercle-ellipse et lignes droites-lignes droites sont explorées. Les cercles et les lignes sont deux des caractéristiques géométriques qui sont principalement présentes dans les structures et les bâtiments. En fonction de la rela-tion entre les caractéristiques 3D et leurs images 2D correspondantes détectées dans l'image, la position et l'orientation de la caméra sont estimées.Augmented reality (AR) had recently made a huge impact on field engineers and workers in construction industry, as well as the way they interact with architectural plans. AR brings in a superimposition of the 3D model of a building onto the 2D image not only as the big picture, but also as an intricate representation of what is going to be built. In order to insert a 3D model, the camera has to be localized regarding its surroundings. Camera localization con-sists of finding the exterior parameters (i.e. its position and orientation) of the camera with respect to the viewed scene and its characteristics. In this thesis, camera pose estimation methods using circle-ellipse and straight line corre-spondences has been investigated. Circles and lines are two of the geometrical features that are mostly present in structures and buildings. Based on the relationship between the 3D features and their corresponding 2D data detected in the image, the position and orientation of the camera is estimated
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose
estimation. Our novel 3D orientation estimation is based on a variant of the
Denoising Autoencoder that is trained on simulated views of a 3D model using
Domain Randomization. This so-called Augmented Autoencoder has several
advantages over existing methods: It does not require real, pose-annotated
training data, generalizes to various test sensors and inherently handles
object and view symmetries. Instead of learning an explicit mapping from input
images to object poses, it provides an implicit representation of object
orientations defined by samples in a latent space. Our pipeline achieves
state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D
domain. We also evaluate on the LineMOD dataset where we can compete with other
synthetically trained approaches. We further increase performance by correcting
3D orientation estimates to account for perspective errors when the object
deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and
Pattern Recognition (CCVPR) 201
Adaptive User Perspective Rendering for Handheld Augmented Reality
Handheld Augmented Reality commonly implements some variant of magic lens
rendering, which turns only a fraction of the user's real environment into AR
while the rest of the environment remains unaffected. Since handheld AR devices
are commonly equipped with video see-through capabilities, AR magic lens
applications often suffer from spatial distortions, because the AR environment
is presented from the perspective of the camera of the mobile device. Recent
approaches counteract this distortion based on estimations of the user's head
position, rendering the scene from the user's perspective. To this end,
approaches usually apply face-tracking algorithms on the front camera of the
mobile device. However, this demands high computational resources and therefore
commonly affects the performance of the application beyond the already high
computational load of AR applications. In this paper, we present a method to
reduce the computational demands for user perspective rendering by applying
lightweight optical flow tracking and an estimation of the user's motion before
head tracking is started. We demonstrate the suitability of our approach for
computationally limited mobile devices and we compare it to device perspective
rendering, to head tracked user perspective rendering, as well as to fixed
point of view user perspective rendering
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